Method for provisioning a wireless network

ABSTRACT

A method that collects data and subjects it to statistical analysis to detect localized events, which assists in network provisioning. Illustratively, the data employed is hourly network traffic count that is collected at cell sites. By taking the advantage of additive property of Poisson process, the method integrates spatial neighbor information by aggregating temporal data in various areas, and iteratively estimating the event location and the radius of event impact by examining the posterior probability base on the aggregated data.

BACKGROUND OF THE INVENTION

This relates to development of information from traffic data of awireless network, where the data includes events that are outsideeveryday network load. This information assists in anticipating futureevents and their geographical impact, and thereby assists inprovisioning network capacity.

With the fast development of mobility network technology, the networktraffic has increased significantly. To improve the mobility networkperformance, service providers have invested significant resources toimprove the coverage, enhance the quality, and increase the capacity. Toillustrate, AT&T has invested more than $1.5 billion from 2007 to 2009in California alone, and a Verizon-led investment group is committing toinvest $1.3 billion in wireless long term evolution development.

Differentiated from wireline network, wireless network quality isrelatively dynamic. It is impacted by the nature of the network's use(e.g., time spent by users to download data from the Internet),retransmit rates that are affected by signal to noise ratios, and by thenature of cell phone use. The easy-to-carry mobile phones are much moreengaged with human activities than the wired phones, and hence thenetwork traffic is heavily influenced by what people do. A verysignificant component in the variability of the wireless network's loadand the perceived quality of service is social events. At large socialevents many cell phone users gather in a small area, such as a sports orconcert venue, and—unless some provision is made—that causes networkcapability to overflow. To illustrate how significant an effect an eventcan have, it is noted that Super Bowl XLIV, for example, which was heldat Sun Life Stadium (Miami Garden, Fla.), attracted about 75,000 people,where the normal population for Miami Garden is a bit over 100,000. Thecall traffic increase is probably much higher than the 175% populationincrease, and such an increase is not something that the wirelessnetwork is typically designed (or should be expected) to handle.

Clearly, it is important to anticipate events. Events such as the SuperBowl are easy to anticipate because they are scheduled months inadvance, but there are many lesser events that cannot be easilyanticipated because they are not scheduled well in advance. One way toanticipate events is to be aware of past events, and to predict futureevents based on the past events. Although many events can be accountedfor from data other than actual network traffic data, a much morecomplete picture can be had by detecting events from the network dataitself.

From a statistical point of view, in general, there are three types ofevent detection methods: outlier/change point based method, patternbased method, and model based method. For the model based eventdetection approach (which underlies the approach of this invention)different models have been constructed based on the characteristics ofthe measured data. For example, the Dynamic Bayesian Networks (DBNs)approach has been applied to detect abnormal events in underground coalmines, Markov random fields (MRFs) have been used to model spatialrelationships at neighboring sensor nodes, and the Hidden Markov Modelhas been used on fMRI (functional Magnetic Resonance Imaging) to detectactivation areas. Ihler et al in “Adaptive Event Detection WithTime-Varying Poisson Processes,” in KDD '06, Proceedings of the 12th ACMSIGKDD international conference on Knowledge discovery and data mining,New York, N.Y., ACM, 2006, pp. 207-216, utilize a Markov Model ModulatedNonhomogeneous Poisson Process to detect events from highway trafficdata collected by one sensor at a specific location.

In recent years, scan statistics have been a hot topic in spatialanalysis and nowadays it appears to be the most effective “hotspots”detection method. The scan statistics are used to test a point processto see if it is purely random, or if any clusters of events are present.There are numerous variations of spatial scan statistics, but they sharethe three basic properties: the geometry of the scanned area, theprobability distributions generating events under null hypothesis, andthe shapes and sizes of the scanning window. The spatial scan statisticsmeasure the log-likelihood ratio for a particular region to test spatialrandomness. The region with the largest spatial scan statistic is themost likely to be generated by a different distribution. By extendingthe scan window from circular to cylindrical, the scan statistic extendsfrom spatial domain to spatiotemporal domain. Scan statistics assumesthat the null hypothesis is known or can be estimated through MonteCarlo simulation; but the null hypothesis assumption is often invalidand, moreover, the Monte Carlo simulation is computationally expensive.

SUMMARY

An advance in the art is realized with a method that collects data andsubjects it to statistical analysis to detect localized events, whichassists in network provisioning. Illustratively, the data employed ishourly network traffic count that is collected at cell sites. By takingthe advantage of additive property of Poisson process, the disclosedmethod integrates spatial neighbor information by aggregating temporaldata in various areas, and iteratively estimating the event location andthe radius of event impact by examining the posterior probability baseon the aggregated data.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 presents pseudo code that represents one embodiment in accordwith the principles disclosed herein, and

FIG. 2 presents a flowchart that represents another embodiment in accordwith the principles disclosed herein, and

FIG. 3 illustrates a functional block diagram of a system consistentwith at least one embodiment of the invention.

DETAILED DESCRIPTION

It is well established that network traffic varies with time of day, andalso varies with days of the week. Identifying events in such networktraffic data is ideally accomplished by learning the normal trafficpattern and special event pattern. However, this problem is challengingdue to unknown baseline for normal traffic as well as unknown eventinformation, such as event location, and event duration. Anotherchallenge is the fact that not only the temporal information, but alsothe spatial information ought to be taken into account.

To accurately assess event influence to network, it is necessary toconsider an event's impact on multiple neighboring cells, where thecells are likely to have different traffic patterns either naturally(e.g., proximity to a train station) or driven by different events. Thelarge amount of data is another difficulty in the network traffic eventdetection the necessary computing might be unfeasible even though thecomputing increases only linearly with data.

Markov Modulated Poisson Process (MMPP) is a popular traffic model forcapturing the characteristic of actual network load by describingvarying rate parameters at irregular intervals according to Markovprocess which, basically asserts that the probability of a data point ina series depends on data points that immediately precede it. In“Bayesian Methods and Extensions for the Two State Markov ModulatedPoisson Process,” S. L. Scott, in Ph.D. dissertation, HarvardUniversity, Dept. of Statistics, 1998, extends the MMPP from homogeneousPoisson process to non-homogeneous Poisson process by embedding multipleperiodic rates in Poisson process, and applies it to network intrusiondetection and web traffic data. Ihler et al in the aforementionedarticle utilize the similar framework on freeway traffic and buildingentrance event detection. All the proposed methods focus on univariatetime series.

To better appreciate the advance of this invention, it may be helpful toreview the Markov Modulated Non-Homogeneous Poisson Process (MMNHPP) formodeling temporal data.

Let N^(i)(t) denote the observed traffic count at cell site i (iε

) and time t, where

denotes the considered region, and cell site i is at location L_(i),which is specified by its latitude, l1_(i) and longitude, l2_(i′), i.e.,the vector L_(i)=[l1_(i),l2_(i)]. The actual traffic load (e.g., callcount) at cell site i, as a function of reporting times, N^(i)(t), canbe represented as N^(i)(t)=N₀ ^(i)(t)+N_(E) ^(i)(t), where N₀ ¹(t) isthe normal traffic load, and N_(E) ^(i)(t) is the increased traffic loaddue to the presence of one or more events. It is sufficient to model N₀^(i)(t) and N_(E) ^(i)(t) instead of modeling N^(i)(t) directly. Tomodel N₀ ^(i)(t) we adopt a non-homogeneous Poisson process with therate λ^(i)(t) which, to incorporate the multiple periodic temporalpatterns in network traffic, is assumed to beλ^(i)(t)=λ₀ ^(i)σ_(d(t)) ^(i)η_(d(t),h(t)) ^(i),  (1)where λ₀ ^(i) is the average rate of the Poisson process over a week atcell site i, σ_(d(t)) ^(i) is the day effect (d(t) indicates theday-of-week, dε{1, 2, . . . , 7}), and η_(d(t),h(t)) ^(i) is the houreffect (h(t) indicates the hour-of-day, hε{0, 1, . . . , 23}). Given asequence of data, we assume that λ₀ ^(i) follows Gamma distribution, andσ_(d(t))η_(d(t),h(t)) follow Dirichlet distribution.

Now we consider the modeling of N_(E) ^(i)(t) for the case where eventsincrease the network traffic during a short consistent period (in someapplications, it may be more reasonable to consider the case whereevents decrease the traffic). To indicate the presence of an event attime t, we use a binary process z(t):

$\begin{matrix}{{z(t)} = \left\{ \begin{matrix}0 & {{if}\mspace{14mu}{there}\mspace{14mu}{is}\mspace{14mu}{no}\mspace{14mu}{event}\mspace{14mu}{at}\mspace{14mu}{time}\mspace{14mu} t} \\1 & {{if}\mspace{14mu}{there}\mspace{14mu}{is}\mspace{14mu}{an}\mspace{14mu}{event}\mspace{14mu}{at}\mspace{14mu}{time}\mspace{14mu} t}\end{matrix} \right.} & (2)\end{matrix}$The probability distribution of z(t) is defined to be a two-state Markovprocess with transition matrix:

$\begin{matrix}{M_{z} = \begin{pmatrix}{1 - z_{o}} & z_{1} \\z_{0} & {1 - z_{1}}\end{pmatrix}} & (3)\end{matrix}$where the expected value for the time between events is 1/z₀ and theexpected value for event duration is 1/z₁. Using z(t), the event countN_(E) ^(i)(t) can be modeled as Poisson with the rate γ^(i)(t)

$\begin{matrix}{{N_{E}^{i}(t)} = \left\{ \begin{matrix}0 & {{z(t)} = 0} \\{P\left( {N^{i};{\gamma^{i}(t)}} \right)} & {{z(t)} = 1.}\end{matrix} \right.} & (4)\end{matrix}$The unknown parameters in the models for N₀ ^(i)(t) and N_(E) ^(i)(t)can be estimated in the Bayesian framework. Essentially, Markov ChainMonte Carlo (MCMC) sampling is used to estimate the posteriorprobability p(z(t)=11|N(t)), as described in the aforementioned Ihler etal article.

Put in layman's terms, in the MMNHPP a given a set of data points isassumed to consist of data points that at times correspond to trafficwith no special events present, and at times correspond to traffic thatmay be including a special event. There is, therefore, a distribution oftraffic load measurements without an event, and a distribution oftraffic load measurements with an event. The challenge is to find theparameters of the two distributions that best fit the data, under theassumption that those distributions are extended Poisson distributions,where the probability of k events in a given period of time is

${{f\left( {k,\lambda} \right)} = \frac{\lambda^{k}{\mathbb{e}}^{- \lambda}}{k!}},$where λ is the expected number of event is that given period of time, itis a function of time t, and is different for the two conditions: withevent, and without event.

Once the best-fitting parameters are chosen, the probability that agiven data point belongs to a non-event data can be computed, and theprobability that the same given data point belongs to event data canalso be computed. If the probability that the given point belongs toevent data is greater than the probability that the given point belongsto non-event data, then the conclusion is reached that the point belongsto an event.

How to find the best parameter estimates for the aforementioneddistributions based on a given corpus of data is well known to artisansin the art of data analysis.

It may be noted that the model discussed above is for a time series at asingle cell site i, and the spatial relations among multiple time seriesare not considered. In accord with the principles disclosed hereinspatial information is taken into account.

Recognizing that “everything is related to everything else, but nearthings are more related than distant things,” our strategy is toaggregate a set of closely located time series of similar pattern.Specifically, we define a neighborhood region around the cell site iwith the radius r as A_(i) ^(r)={j; ∥L_(J)−L_(i)∥<r}, where∥L_(j)−L_(i)∥ represents the geographic distance between cell sites iand j. That is, the neighborhood of cell site i, A_(i) ^(r), encompassesat least one additional site.

The observations in A_(i) ^(r) can be represented asN ^(A) ^(i) ^(r) =N ₀ ^(A) ^(i) ^(r) +N _(E) ^(A) ^(i) ^(r)   (5)where N₀ ^(A) ^(i) ^(r) and N_(E) ^(A) ^(i) ^(r) are the total normaland event observations for all time series in area A_(i) ^(r)respectively. Thus, N₀ ^(A) ^(i) ^(r) and N₀ ^(A) ^(i) ^(r) can berepresented as

${N_{0}^{A_{i}^{r}} = {{\sum\limits_{j \in A_{i}^{r}}{{N_{0}^{j}(t)}\mspace{14mu}{and}\mspace{14mu} N_{E}^{A_{i}^{r}}}} = {\sum\limits_{j \in A_{i}^{r}}{N_{E}^{j}(t)}}}},$respectively.

According to the additive property of the non-homogeneous Poissonprocess, N₀ ^(A) ^(i) ^(r) is also a non-homogeneous Poisson processgiven that all time series in A_(i) ^(r) are independent Poisson randomvariables. Hence, the count rate for the neighborhood region A_(i) ^(r)can be expressed as

$\begin{matrix}\begin{matrix}{{\lambda_{0}^{A_{i}^{r}}(t)} = {\sum\limits_{j \in A_{i}^{r}}{\lambda_{0}^{j}(t)}}} \\{= {\sum\limits_{j \in A_{i}^{r}}{{\lambda_{0}^{j}(t)}\sigma_{d{(t)}}^{j}{{h(t)}.}}}}\end{matrix} & (6)\end{matrix}$We assume that if an event happens at a particular time, it affects theneighboring cells roughly simultaneously, i.e., the starting and endingpoints of the increased traffic for the affected cells are assumed to besimilar enough. Then the temporal Markov process transition matrices forthe neighboring cells are also assumed to be the same. We also utilizethe fact that driven by the propagation of cellular radio signal, thefarther the distance from the signal source, the weaker the signalstrength. It is known that if the signal originates from a locationL_(e), then the strength of the signal at location L_(i) can beexpressed as

$\frac{a}{{{L_{e} - L_{i}}}^{n}},$where n is a value between 2 and 4 depending on geography conditions,and a is a constant.

In addition to the cellular radio propagation property, from engineeringdesign view, the closer the cell site to the signal, the higher thepriority it has. The signal will be picked by farther cell only if thecloser one is in overflow. Driven by the above, the spatial impact of anevent can be modeled as binary function that relates the currentlocation and the distance from where the event happens.

When an event takes place at location e, the event's impact at locationi, can be expressed as a binary process, as follows:

$\begin{matrix}{s_{i}^{e} = \left\{ \begin{matrix}{0,} & {{{L_{e} - L_{i}}} > R_{e}} \\{1,} & {{{L_{e} - L_{i}}} \leq R_{e}}\end{matrix} \right.} & (7)\end{matrix}$where R_(e) is the radius of event impact. Combining (1) and (6), inspatiotemporal domain the presence of an event can be indicated by theproduct of z(t) and s_(i) ^(e). That is, if we let

$\begin{matrix}{{ST}_{it}^{e} = \left\{ \begin{matrix}{0,} & {{{{L_{e} - L_{i}}} > {R_{e}\mspace{14mu}{or}\mspace{14mu}{z(t)}}} = 0} \\{1,} & {{{{L_{e} - L_{i}}} \leq {R_{e}\mspace{14mu}{and}\mspace{14mu}{z(t)}}} = 1}\end{matrix} \right.} & (8)\end{matrix}$equation (8) implies that the event originates from e only impacts thecells in the area A_(e) ^(R) ^(e) while z(t)=1. Using equation (8), theincreased observations due to the event can be modeled as a Poissonprocess:

$\begin{matrix}{\left. {N_{E}^{A_{e}^{R_{e}}}(t)} \right.\sim\left\{ \begin{matrix}{0,} & {{{while}\mspace{14mu}{ST}_{it}^{e}} = 0} \\{P\left( {N;{\sum\limits_{j \in A_{e}^{R_{e}}}{\gamma^{j}(t)}}} \right)} & {{{while}\mspace{14mu}{ST}_{it}^{e}} = 1}\end{matrix} \right.} & (9)\end{matrix}$From equations (8) and (9) is it obvious that an event impact isexplained by its temporal duration, event location, and the impactradius. As discussed above, the temporal durations are assumed to be thesame for all cells in the impacted area A_(e) ^(R) ^(e) and, hence, theduration can be estimated by Markov process using any of time series inthe area A_(e) ^(R) ^(e) .

The method disclosed herein comprises collecting data, processing toobtain results, and utilizing the results. The processing, in turn,comprises initialization, aggregation, estimation, re-centralization,and adaptation, where the initialization, aggregation, estimation,re-centralization, and adaptation are iteratively repeated until apreselected criterion is met. The key tasks in each stage are presentedin the following.

Initialization: Overall network traffic data is very voluminous soexamining all these cell sites is prohibitively expensive in terms ofcomputation cost. Therefore, in accord with one aspect of our disclosurewe begin by identifying a subset of cell sites where a very simple testreveals that at least one event impacts the traffic. This isaccomplished by choosing those cells whose peak traffic during theobserved time period (the time series of data for the cell) exceeds thetraffic value during a chosen portion of the observed time period (e.g.,during 75% of the time period), by a preselected amount, and ranking theidentified cells by the computed traffic level. We call the identifiedset the “seed” cell sites, or time series. We note that each eventreveals itself as a “seed” in one or more cell sites. We also noted thatthe above-described approach for limiting the number of cells that areconsidered is merely illustrative, and that other approaches can be usedwithout departing from the spirit and scope of this invention.

Aggregation: Choosing each cell in the seed time series we aggregate thedata from all cells in a defined neighborhood of the chosen cell, and inan iterative process we increase the size of the neighborhood until apredetermined condition occurs (e.g., acquiring, albeit the smallestnumber of, cell sites).

Estimation: By fitting the aggregated time series with MMNHPP, wecompute the posterior probability of event p(z(t)=1). If p(z(t)=1)>0.5for time [t_(s), t_(e)] (i.e., time t spanning t_(s)-t_(e), andt_(e)-t_(s)>h hours (where h, for example, is 3 hours)), then weconsider that all of aggregated the time series are affected by someevent during the time period of [t_(s), t_(e)]; i.e., t_(s) and t_(e)the starting and ending time points of the event, respectively.Otherwise, we regard it as an outlier. For example, if there is a onehour spike in traffic, we do not consider that to be an event. For aspecific detected event, the corresponding time series that wereaggregated are the members of the cluster for the event.

Centralization: For each detected event cluster, we compute the centerpoint by taking an average of the coordinates for all cells that areincluded in the cluster. The computed center point may be a simplegeographical center of all cell sites in the cluster. The geographicalcenter calculation is constrained to not exceed certain bounds, such asto not move farther away from the location of the seed cell than acertain distance. Alternatively, the geographical center can be aweighted calculation that takes account of both geographical locationsand traffic levels.

Adaptation: We set the computed center as the new point about which aneighborhood is computed, and return to the step of aggregating the datafrom neighboring cells (starting with the existing size of theneighborhood). Repeat the aggregation, estimation, and centralizationsteps until a cell site (and a corresponding time series) is encompassedby the neighborhood but is not impacted by the event underconsideration. The underlying assumption here is that if a set of timeseries with no event is aggregated to the time series with an event X,the effect of event X will be masked somewhat, and we will have reducedprobability of concluding that an event occurs. In other words, thedecision as to whether to further expand the neighborhood is based onstatistical analysis of the aggregated time series (as disclosed above).

The result of the above process is that the center where the event isperceived to originate (in a center of gravity sense), the spatialimpact of the event, and the temporal impact of the event become known.

It should be understood that there are various approaches for addressingall of the events that are contained in the data. That includessearching for event-impacts in time order, in order of seed strengths,or in any other order (including random).

FIG. 1 presents the pseudocode of one embodiment in accord with theprinciples disclosed herein, where the seed sites are sorted bystrength, but once a seed site is under investigation, all events thatimpact that site are considered. Although the pseudocode is straightforward and self-explanatory, the following presents a relativelydetailed explanation of the code.

In line 1, the variable m corresponds to the number of cell sites whosetraffic data has been collected, max(N^(i)) is the maximum traffic load(e.g., number of calls) at cell site i, at some reporting time interval(e.g., hour), t. Q_(x)(N^(i)) is the traffic level that is not exceededfor x percent of the time, and TH is a chosen threshold. For example,the maximum number of calls in a certain hour of a certain date cellsite i might be 1021 (i.e., max(N^(i))=1021), whereas during 75% of allother hours the number of calls in cell i does not exceed 750 (i.e.,Q₇₅(N^(i))=750). Line 4 results in a sorted set of traffic load valuesD^(i), each thus identifying cell i where at least one event has animpact. These are seed cell sites.

Line 5 begins the process for considering each of the seed cell sitesare considered, starting with a first cell site (i=1), and line 6identifies spans of reporting times, or groups of consecutive reportingtimes, that correspond to times in the considered cell site during whichthere are event impacts. The number of consecutive reporting times thatmust be found to constitute a group is a preselected constant, h, suchas the aforementioned three hours. The number of such groups is E^(i)(line 7).

Line 8 begins the process of considering each of the events in theconsidered cell site, starting with the earliest event, (j=1).

Line 9 computes the duration of event. That is, since a particulargrouping of reporting times is considered, the duration of an event isdefined by the starting time, and the stopping time; i.e., by [t_(s),t_(e)].

Line 10 evaluates whether the computed duration of event j is close (asfar geographical location as well as starting and stopping times) to analready considered event that control passes to line 11, which adds thesite of the seed to the corresponding cluster, increments index j andline 12 returns control to line 9 (to assess the event next in time onsite i. Otherwise, control passes to line.

Line 14 initializes the iterative process, by setting the centerlocation of the event at the geographical location of the cell site i(that is, the geographical coordinates of the cell site's antenna). Italso chooses a circular area of a small initial radius r₀, sets thenumber of sites within that area, C^(A) ^(c) ^(r) , at 1, sets theduration of the event relative to the center to the duration at cellsite i, and sets the change of overall probability of an event impactingwithin the area centered on center c to some number a>0.

The iterative process proceeds by testing whether the duration of theevent relative to the center is approximately the same as the durationof the event relative to the geographical location of the cell site i,and whether the change of overall probability of an event impactingwithin the area centered on center c is positive. If so—and it is so inthe first iterative loop by the initialization process—then the steps oflines 16-19 increase the radius (and thereby increase the area) until atleast one other cell sites is found within the area.

At this point, line 21 computes a new geographical center (from thegeographical coordinates of the encompassed cell sites) and line 22 sumsthe traffic loads of the encompassed cell sites. Line 23 employs theaforementioned statistical means for computing the duration of the eventrelative to the new center. Lastly, line 27 outputs the duration andexpanse of the events.

FIG. 2 presents a block diagram of a second embodiment. Block 100identifies the seeds pursuant to a process that, for example, followssteps 1-4 of FIG. 1. It is noted that any particular cell may have morethan one seed (at different reporting times). The result is a set ofseeds that, optionally, are not even sorted. Control then passes toblock 102 where a first seed in the set is chosen (by setting variable ito 1), and control passes to block 104. Block 104 notes the geographicallocation of the cell site to which the chosen seed belongs and sets thecurrent perception of the center of the event that is reflected by theseed to that geographical location. Control then passes to block 106which identifies the duration of the event (i.e. the set of consecutivereporting times that correspond to the event) using the statisticalapproach disclosed above.

It is possible that the considered seed (other than the very first seed,of course) was already accounted for while considering a previous seed.Therefore, control passes from block 106 to block 108, which considerswhether the seed i that is to be currently considered has beenpreviously handled when considering a previous seed, j. If it has beenpreviously considered, control passes to block 124. Otherwise, controlpasses to block 110, which defines the smallest area (for example, acircular area about the event's perceived center with the smallestradius) that results in the addition of a cell site (from among allgeographically neighboring cell sites; that is, Δk>0, where k is thenumber of cell sites that are encompassed by the chosen area. It shouldbe understood that the initial area or territory that is chosen is onethat includes the site where the seed event resides plus some minimumnumber of additional sites. That area can be circular, thus beingdefined by a radius, and the radius can begin with some minimum valuethat is iteratively increased until another site is encompassed, or itcan start with an incremental increase over the distance from the sitewhere the seed event resides to the geographically next closest site. Itis understood, of course, that an increase of the neighborhood thatresults in the addition of one cell site may result in the addition of anumber of cell sites.

From block 110 control passes to block 112 where the traffic loads ofthe cell sites encompassed by the chosen area are aggregated (e.g.,summed), and control passes to block 114, which determines, based on thestatistical approach disclosed above (see line 23 in FIG. 1) whether thetraffic count that is attributable to the event has increased. If so,control passes to block 114 where the event's duration is re-determined,and control passes to block 116, which computes the event's duration andpasses control to block 118, which enlarges the geographical area ofinvestigation only so much as to increase the number of encompassed cellsites. Control then passes to block 120 which re-computes the perceivedevent center and re-establishes the set of cell sites encompassed by theexisting neighborhood size before returning control to block 112.

When block 114 concludes in the negative (that the traffic countattributable to the event has not increased), control passes to block122, which stores the information about the addressed event, itsperceived center and reach (i.e., the last-employed radius), and passescontrol to block 124. Block 124 determines whether the addressed seed isthe last seed to be addressed. If not, control passes to block 128 whichincrements index I and returns control to block 104. Otherwise, controlpasses to block 126, which sends the information stored by block 124 towhatever network provisioning system the user of the method desires.This may comprise, for example, a schedule that is created from thestored information for delivering portable cells (“cells on wheels”) tothe appropriate geographical areas so that they can be employed whenfuture events are expected.

A method, executed on a processing platform 302, for enabling a wirelessnetwork 308 to be provisioned, includes collecting from access points(APs) 306 of said network, traffic volume data in successive timeintervals. The method includes identifying event seeds, which are thoseof said APs where during one or more time spans of a preselected numbercontiguous ones of said time intervals the traffic volume in said timeintervals exceeds a chosen threshold. The method includes processingeach of the event seeds, through iterative processing cycles, toidentify for each of said event seeds an estimated event center andassociated event impact territory. The method includes providinginformation about the events identified by said processing to aprovisioning system 304 of wireless network 308. The territory may beexpressed by a radius of a circle 310 centered about said associatedevent center. The processing in each of the processing cycles mayinclude statistical analysis. The statistical analysis may be a MarkovModulated Nonhomogenous Poisson Process that is employed to estimateprobability with which said traffic data is indicative of an event. Eachof the iterative processing cycles may include statistical analysis, andeach such cycle may begin with one of said event seeds, which occurs ata particular AP, an estimated event center that is associated withgeographical coordinates of said particular AP, and a territory thatencompasses said particular AP and a minimum number, greater than zero,of neighboring APs, and at each of said cycles enlarging said territoryand augmenting said estimated event center based on the APs encompassedby said enlarged territory, until a preselected condition is met thatterminates said iterative processing. The territory may be defined by aradius of a circle about said event center, and at each cycle of theiterative processing said radius is enlarged only so much as toencompass a minimum number of APs that were not already encompassed inthe immediately previous cycle. The condition may be met with trafficattributed to said event is not increased with the enlarged territory.

The above embodiments descriptions are, of course, illustrative, andother embodiments can be realized by a person skilled in the art. Forexample, Additionally, while the above disclosure is couched in terms ofa cellular network and cell sites, it should be realized that this, too,is merely illustrative and that any access point (AP) via which trafficcan enter the telecommunication network, can serve as data sources

The invention claimed is:
 1. A method, executed on a processingplatform, for enabling a wireless network to be provisioned, comprising:collecting, from access points of said network, traffic volume data insuccessive time intervals; identifying as event seeds, those of saidaccess points having, during one or more time spans of a preselectednumber of said successive time intervals, traffic volume that exceeds athreshold; processing each of the event seeds to determine informationincluding an estimated even center and associated event impactterritory, the processing being iterative, each iteration of theprocessing including statistical analysis, and using the estimated eventcenter and a minimum number, greater than zero, of neighboring accesspoints, each of said iterations enlarging said event impact territoryand augmenting said event impact territory, until a preselectedcondition is met that terminates the processing; and providing theinformation to a provisioning system of said wireless network.
 2. Themethod of claim 1 where said event impact territory is expressed by aradius of a circle centered about said associated estimated eventcenter.
 3. The method of claim 1 where said event impact territory isdefined by a radius of a circle about said estimated event center, andeach iteration of the processing enlarges said radius only so much as toencompass a minimum number of access points that were not alreadyencompassed in the immediately previous iteration.
 4. The method ofclaim 1 further comprising: creating a schedule for delivering portablecell sites to geographical areas based on the information.
 5. The methodof claim 1 wherein identifying the estimated event center uses aweighted calculation based on geographical locations of the accesspoints in a corresponding event seed of the event seeds and associatedtraffic levels.
 6. A method, executed on a processing platform, forenabling a wireless network to be provisioned, comprising: collecting,from access points of said network, traffic volume data in successivetime intervals; identifying as event seeds, those of said access pointshaving, during one or more time spans of a preselected number of saidsuccessive time intervals, traffic volume that exceeds a threshold;processing each of the event seeds to determine information including anestimated event center and associated event impact territory; andproviding the information to a provisioning system of said wirelessnetwork, wherein identifying the estimated event center is based on anaverage of coordinates for all access points in a corresponding eventseed of the event seeds.
 7. The method of claim 6 where the processingis iterative and each iteration of the processing includes statisticalanalysis.
 8. The method of claim 7 where said statistical analysis is aMarkov Modulated Nonhomogenous Poisson Process that is employed toestimate probability with which said traffic volume data is indicativeof an event.
 9. The method of claim 6 where the processing is iterativeand each iteration of the processing includes statistical analysis, andusing the estimated event center and a minimum number, greater thanzero, of neighboring access points, each of said iterations enlargessaid event impact territory and augments said event impact territory,until a preselected condition is met that terminates said processing.10. The method of claim 9 where said condition is met with trafficattributed to said event is not increased with the enlarged territory.11. The method of claim 9 wherein the preselected condition includes theevent impact territory including an access point that is not impacted byan event associated with a corresponding event seed of the event seeds.12. An apparatus comprising: a processing platform configured to collecttraffic volume data in successive time intervals from access points,identify as event seeds, those of said access points having, during oneor more time spans of a preselected number of said successive timeintervals, traffic volume that exceeds a threshold, and process eachaccess point of the event seeds to identify an estimated event centerand associated event impact territory, where the processing platform isconfigured to iteratively process each access point and each iterationof the processing includes statistical analysis, and using the estimatedevent center and a minimum number, greater than zero, of neighboringaccess points, each of said iterations enlarges said event impactterritory and augments said event impact territory, until a preselectedcondition is met that terminates the processing; and a provisioningsystem configured to provision a wireless network based on theinformation about the event seeds.
 13. The apparatus of claim 12 wheresaid event impact territory is defined by a radius of a circle aboutsaid estimated event center, and the processing is iterative and eachiteration of the processing enlarges said radius only so much as toencompass a minimum number of access points that were not alreadyencompassed in the immediately previous iteration.
 14. The apparatus ofclaim 12 wherein identifying the estimated event center is based on anaverage of coordinates for all access points in a corresponding eventseed of the event seeds.